Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading

Ahmad, Afaq; Lagaros, Nikos D.; Cotsovos, Demetrios M.

doi:10.3390/app11114975

Cited by 10 publications

(8 citation statements)

References 42 publications

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“…This study uses three such techniques based on machine learning including feed FNN, particle swarm optimization-based FNN (PSOFNN), and bat algorithm-based FNN (BATFNN). The author's previous work [42] included only ANN as a soft computing approach for determining the punching shear strength of flat slabs and since then the ACI code has also been revised. This study includes two more techniques of PSOFNN and BATFNN and an updated version of ACI 318-19 for calculating the shear strength of flat slabs.…”

Section: Soft Computing Methodsmentioning

confidence: 99%

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Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Wahab,

Mahmoudabadi,

Waqas

et al. 2024

Advances in Civil Engineering

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This research focuses on a comprehensive comparative analysis of shear strength prediction in slab–column connections, integrating machine learning, design codes, and finite element analysis (FEA). The existing empirical models lack the influencing parameters that decrease their prediction accuracy. In this paper, current design codes of American Concrete Institute 318-19 (ACI 318-19) and Eurocode 2 (EC2), as well as innovative approaches like the compressive force path method and machine learning models are employed to predict the punching shear strength using a comprehensive database of 610 samples. The database consists of seven key parameters including slab depth (ds), column dimension (cs), shear span ratio (av/d), yield strength of longitudinal steel (fy), longitudinal reinforcement ratio (ρl), ultimate load-carrying capacity (Vu), and concrete compressive strength (fc). Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. PSOFNN predicted the punching shear of flat slab with maximum accuracy with R2 value of 99.37% and least MSE and MAE values of 0.0275% and 1.214%, respectively. The findings of the study are validated through FEA of slabs to confirm experimental results and machine learning predictions that showed excellent agreement with PSOFNN predictions. The research also provides insight into the application of metaheuristic models along with ANN, revealing that not all metaheuristic models can outperform ANN as usually perceived. The study also highlights superior predictive capabilities of EC2 over ACI 318-19 for punching shear values.

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Section: Soft Computing Methodsmentioning

confidence: 99%

“…The correlation influence is represented by the intensity of color in the heatmap accompanied by correlation coefficient values. The dark color represents a strong correlation and a correlation value closer to +1, while a light color represents a weaker correlation with a correlation coefficient closer to 0[42,51].…”

mentioning

confidence: 99%

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Wahab,

Mahmoudabadi,

Waqas

et al. 2024

Advances in Civil Engineering

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“…The ANN model (with functional parameters mentioned in Table 4) is trained as a multi-layer model that is coded in MATLAB using the Levenberg-Marquardt algorithm with a free forward backpropagation method [4,19,20]. The key aspects of training are summarised as follows:…”

Section: The Functionality Of the Ann Modelmentioning

confidence: 99%

“…Researchers and engineers have put forward various essential theories [1][2][3] and techniques [4] to precisely forecast the behaviour of reinforced concrete (RC) structure elements at the ultimate limit state (ULS) to make both a safe and economic structure. New analysis techniques have been introduced in structural analysis to provide more precise solutions to rising complicated problems with cost effectiveness without the need for a physical model.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Ahmad,

Elahi,

Barbhuiya

2023

Materials

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This research aims to conduct a comparative analysis of the first crack load, flexural strength, and shear strength in reinforced concrete beams without stirrups. The comparison is made between the conventional model developed according to the current design code (ACI building code) and an unconventional approach using Artificial Neural Networks (ANNs). To accomplish this, a dataset comprising 110 samples of reinforced concrete beams without stirrup reinforcement was collected and utilised to train a Multilayer Backpropagation Neural Network in MATLAB. The primary objective of this work is to establish a knowledge-based structural analysis model capable of accurately predicting the responses of reinforced concrete structures. The coefficient of determination obtained from this comparison yields values of 0.9404 for the first cracking load, 0.9756 for flexural strength, and 0.9787 for shear strength. Through an assessment of the coefficient of determination and linear regression coefficients, it becomes evident that the ANN model produces results that closely align with those obtained from the conventional model. This demonstrates the ANN’s potential for precise prediction of the structural behaviour of reinforced concrete beams.

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“…Another algorithm with nonlinear static and dynamic analyzes used in the design optimization of the structures is PSO [41]. In some studies, ANN and PSO were used together [42][43][44][45][46]. In addition, there are several recent studies that present an analytical-mechanical-based procedure to estimate the seismic behavior of existing buildings [47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

et al. 2023

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The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, target displacements were obtained by performing pushover analysis for a sample reinforced-concrete building model, taking into account 60 different peak ground accelerations for each of the five different stories. Three different target displacements were obtained for damage estimation, such as damage limitation (DL), significant damage (SD), and near collapse (NC), obtained for each peak ground acceleration for five different numbers of stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model to predict target displacements under different seismic risks for mid-rise regular reinforced-concrete buildings, which make up a large part of the existing building stock, using all the data obtained. For this purpose, a hybrid structure was established with the particle swarm optimization algorithm (PSO), and the network structure’s hyper parameters were optimized. Three different hybrid models were created in order to predict the target displacements most successfully. It was found that the ANN established with particles with the best position revealed by the hybrid models produced successful results in the calculation of the performance score. The created hybrid models produced 99% successful results in DL estimation, 99% in SD estimation, and 99% in NC estimation in determining target displacements in mid-rise regular reinforced-concrete buildings. The hybrid model also revealed which parameters should be used in ANN for estimating target displacements under different seismic risks.

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Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading

Cited by 10 publications

References 42 publications

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

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